This problem is not unique to malware detection. To overcome the challenge of small and incomprehensive datasets, many researchers have successfully resorted to data augmentation to artificially grow their datasets and has been especially prominent in the computer vision communities with techniques such as image cropping and scaling [1]}, [2]}, [3]}. In contrast, augmentation techniques for malware datasets have been almost non-existing and operate over feature representations (instead of actual data) due to the difficulty in analyzing and reasoning about raw binaries [4]}, [5]}, [6]}.
